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Treatment Pattern of Type 2 Diabetes Differs in Two ^ 
German Regions and with Patients' Socioeconomic cros^rk 
Position 

Teresa Tamayo\ Heiner Claessen\ Ina-Maria Riickert^, Werner Maier^, Michaela Schunk^, 
Christine IVIeisinger^, Andreas Mielck^, Rolf Holle^, Barbara Thorand^'^, Maria Narres\ Susanne Moebus^, 
Amir-Abbas Mahabadi^, Noreen Pundt^ Bastian Krone^, Uta Slomiany^ Raimund Erbel^, 
Karl-Heinz Jockel^ Wolfgang Rathmann^ Andrea Icks^'^* 

1 Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Dusseldorf, Germany, 2 Institute 
of Epidemiology II, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany, 3 Institute of Health Economics and Health 
Care Management, Helmholtz Zentrum Munchen, Neuherberg, Germany, 4 German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany, 5 Institute for Medical 
Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany, 6 Department of Cardiology, West-German Heart 
Center, University of Duisburg-Essen, Essen, Germany, 7 Department of Public Health, Faculty of Medicine, Heinrich-Helne University Dusseldorf, Dusseldorf, Germany 



Abstract 

Background: Diabetes treatment may differ by region and patients' socioeconomic position. This may be particularly true 
for newer drugs. However, data are highly limited. 

Methods: We examined pooled individual data of two population-based German studies, KORA F4 (Cooperative Health 
Research in the Region of Augsburg, south), and the HNR (Heinz Nixdorf Recall study, west) both carried out 2006 to 2008. 
To ascertain the association between region and educational level with anti-hyperglycemic medication we fitted poisson 
regression models with robust error variance for any and newer anti-hyperglycemic medication, adjusting for age, sex, 
diabetes duration, BMI, cardiovascular disease, lifestyle, and insurance status. 

Results: The examined sample comprised 662 participants with self-reported type 2 diabetes (KORA F4: 83 women, 1 1 1 
men; HNR: 183 women, 285 men). The probability to receive any anti-hyperglycemic drug as well as to be treated with 
newer anti-hyperglycemic drugs such as insulin analogues, thiazolidinediones, or glinides was significantly increased in 
southern compared to western Germany (prevalence ratio (PR); 95% CI: 1.12; 1.02-1.22, 1.52;1. 10-2.11 respectively). 
Individuals with lower educational level tended to receive anti-hyperglycemic drugs more likely than their better educated 
counterparts (PR; 95% CI univariable: 1.10; 0.99-1.22; fully adjusted: 1.10; 0.98-1.23). In contrast, lower education was 
associated with a lower estimated probability to receive newer drugs among those with any anti-hyperglycemic drugs (PR 
low vs. high education: 0.66; 0.48-0.91; fully adjusted: 0.68; 0.47-0.996). 

Conc/usions:\Ne found regional and individual social disparities in overall and newer anti-hyperglycemic medication which 
were not explained by other confounders. Further research is needed. 



Citation: Tamayo T, Claessen H, Ruckert l-M, Maier W, Schunk M, et al. (2014} Treatment Pattern of Type 2 Diabetes Differs in Two German Regions and with 
Patients' Socioeconomic Position. PLoS ONE 9(6): e99773. doi:10.1371/journal.pone.0099773 

Editor: Heiner K. Berthold, Bielefeld Evangelical Hospital, Germany 

Received November 19, 2013; Accepted May 19, 2014; Published June 10, 2014 

Copyright: © 2014 Tamayo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits 
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 

Funding: This work was supported by the Competence Network Diabetes mellitus of the German Federal Ministry of Education and Research (BMBF, grant 
01 GI081 6). Andrea Icks is guarantor of this work. The authors thank the Heinz Nixdorf Foundation (Germany) for the generous support of the Heinz Nixdorf Recall 
Study (HNR). The study is also supported by the German Ministry of Education and Science. The KORA F4 research platform (KORA F4, Cooperative Health 
Research in the Region of Augsburg) was initiated and financed by the Helmholtz Zentrum Mijnchen - German Research Center for Environmental Health, which 
is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. The KORA Diabetes Study was partly funded by a German 
Research Foundation project grant to W.R. from the German Diabetes Center. The German Diabetes Center is funded by the German Federal Ministry of Health, 
and the Ministry of School, Science and Research of the State of North-Rhine-Westfalia. The funders had no role in study design, data collection and analysis, 
decision to publish, or preparation of the manuscript. 

Competing interests: The authors have declared that no competing interests exist. 
* E-mail: andrea.lcks@ddz.uni-duesseldorf.de 



Introduction 

Regional differences in treatment patterns, in particular for drug 
prescriptions, receive growing attention in several countries [1,2]. 
However, data are stUl scarce. This is also true for regional 
differences in anti-hyperglycemic treatment. 



During the last years, new treatment options for type 2 diabetes 
arose. While newer medications such as glitazones, glinides and 
insulin analogues enrich treatment options, metformin (bigua- 
nides) remains the oral drug of first choice in type 2 diabetes 
treatment [3,4]. Little is known about regional differences in 
prescriptions of newer anti-hyperglycemic drugs. 



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Regional Differences in Anti-Hyperglycemic Treatment 



Furthermore, the association between individuals' socioeco- 
nomic position and patterns of medication are of increasing 
interest. Despite the wide literature on the general topic, 
socioeconomic factors are rarely examined in association with 
drug treatment, particularly in the context of anti-hyperglycemic 
medication. We found only one Canadian study which could show 
that patients' income had an important impact on the probability 
of receiving newer thiazolidinediones (TZDs) [5,6] . 

In Germany, about 90% of all individuals are covered by a 
statutory health insurance which reimburses aU medical services 
covering both newer and older diabetes medications [7,8]. 
However, in order to provide economic efficiency, benchmarks 
for budgeting are defined in collective agreements between 
statutory health insurances and physicians [8] . Resident physicians 
can also conclude selective contracts with providers of statutory 
health insurances which may include further tools for guidance. 
About 10% of the population are privately health insured (e.g. self- 
employed individuals, civil servants and their family members). 
These private health insurances impose less economic regulations 
on physicians and offer some extra services basically to provide 
more convenience to patients (e.g. single-bed rooms for inpatient 
treatment, medical attention by a chief physician). For statutory 
health insured patients, a disease management program (DMP) for 
diabetes has been implemented in 2002 [9], covering a large 
proportion of voluntarily participating patients with diabetes. This 
DMP harmonizes diabetes management and provides financial 
compensation for (also voluntarily) participating physicians. 
Within the DMP program quality standards have been defined 
such as HbAlc targets, prevention of hypoglycemic episodes and 
other emergency situations, treatment of hypertension, reduction 
of tobacco consumption among patients, increasing numbers of 
patients who receive disease-specific education [10]. Physicians are 
regularly informed about the average achievement of thesi; goals 
among their patients in comparison to aU registered patients. 
Regarding anti-hyperglycemic treatment, metformin is explicitiy 
recommended in overweight patients with oral monotherapy. 
However, individual treatment decisions (in order to reach the 
aforementioned goals) are supported [10]. Thus, it may be 
assumed that under these conditions, a rather homogenous 
treatment pattern exists. However, in an earlier study of the 
Diabetes Collaborative Research of Epidemiologic Studies (DIAB- 
CORE) consortium, based on pooled individual population-based 
data, self-reported anti-hyperglycemic medication differed across 
regions, without showing a clear geographical pattern [11]. Also, 
the regional population-based studies used for analysis were 
conducted between 1999 and 2006, when disease management 
programs were not widespread. In addition, newer treatment 
options only just arose so that disparities in insulin analogues or 
newer oral anti-hyperglycemic drugs have not yet been examined. 

The aim of our study was to examine (i) if the previously found 
regional differences in anti-hyperglycemic treatment still exist at a 
more recent date, (ii) if general regional disparities in treatment 
patterns exist, e.g. the proportion of patients who receive anti- 
hyperglycemic drugs, but also drug patterns, e.g. prescription of 
newer drugs, and (iii) if treatment patterns differ with patients' 
individual socioeconomic status. We used population-based follow- 
up data from two German regions, one in the south and one in the 
west which have been carried out in a comparable time frame 
between 2006 and 2008. 



Materials and Methods 

Ethics Statement 

The Heinz NLxdorf Recall (HNR) study, including the study 
protocols for participant recruitment, and the informed consent 
for participants, were approved by the institutional local ethical 
committees (baseline: Medical faculty University of Essen; follow- 
up: Medical faculty University of Duisburg-Essen). A quality 
management system according to European industriad norms (DIN 
EN ISO 9001:2000) was applied. All participants gave their 
\\Titten consent. 

In the KORA studies the participants provided written 
informed consent. The ethics committee (Bayerische Landesarzte- 
kammer) approved the study and approved the consent procedure 
including patient information materials and consent form. 

Study population and ascertainment of diabetes 

Two studies were included: the first follow-up of the Heinz 

Nixdorf Recall Study (HNR) which was conducted in the adjacent 
cities of Essen, Bochum and Miilheim of the Ruhr-Area (North 
Rhine- Westphalia, western Germany) and the first follow-up of the 
Cooperative Health Research in the Region of Augsburg Survey 
(KORA F4) study, covering the city of Augsburg and two 
surrounding rural districts (Bavaria, southern Germany). 

4,261 participants attended baseline examinations in KORA S4 
(1999-2001; 2,5-74 years; response 66.8%) [12] and 4.814 
participants in HNR (2000-2003; 45-74 years; response 55.8%) 
[13]. Of these, 3080 participated in the F4 foUow-up study in 
KORA (2006-2008, response 79.6%) [14], and 4,146 in HNR 
(2005-2008, 86.1% response) [15,16]. To aUow for comparability, 
people aged at least 50 years at follow-up were included. Further 
details of the KORA F4 study and HNR have been described 
elsewhere [16-18]. 

Prevalent diabetes was defined based on self-report of a 
physician's diagnosis or self-reported anti-hyperglycemic treatment 
(insulin or oral glucose lowering agents). Study objectives address 
mainly type 2 diabetes. Since distinction of type 1 and type 2 
diabetes is not highly vahd in self-reports, subjects with self- 
reported age at diagnosis before the age of 30 years - possibly 
cases with type 1 diabetes - were excluded. Self-reported age at 
diagnosis was ascertainc-d in both studic-s. Diabetes duration was 
calculated as the timeframe between age at follow-up and age at 
diagnosis. 

Overall, 199 participants had self-reported diabetes in KORA 
F4 and 492 in HNR. After exclusion of 26 participants with 
missing information in relevant data or limited comparability 
(n = 5 in KORA F4, n = 24 in HNR), 194 individuals with type 2 
diabetes from KORA F4 and 468 participants from HNR 
remained for analysis. 

Assessment and classification of prescribed anti- 
hyperglycemic drugs 

Participants were asked to bring the original packages of all 
medications used during the seven days prior to the interview to 
the examination center. Using a scanning system, unique 
pharmaceutical identifiers were recorded assigning ATC codes 
(Anatomical Therapeutic Chemical Classification System) which 
are displayed by 7 characters. The first three characters of the 
ATC code "AlO" indicate any kind of diabetes medication. Under 
"AlOA" all types of insulin are subsumed, while oral anti- 
hyperglycemic agents start with the code "AlOB". 

Following Waugh et al. [3], a drug was considered as "newer" if 
it belonged to the following groups of glucose lowering drugs: 



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I) Insulin analogues: Lispro (A10AB04, A10AC04), combina- 
tions with Lispro (A10AD04), Aspart (A10AB05, A10AC05, 
A10AD05), Glulisine (A10AB06), Glargine (A10AE04), 
Detemir (A10AE05). 

II) Newer orEil anti-hyperglycemic medications: thiazolidine- 
diones (AlOBG: e.g. Rosiglitazone, Pioglitazone), glinides 
(AlOBX: e.g. Repaglinide, Nateglinide), DPP4-inhibitors 
(AlOBH), and combinations of thiazolidinediones or glinides 
with metformin or glimepiride (A10BD03-A10BD08). 

All other anti-hyperglycemic drugs were classified as older 
medications. 

The examined newer anti-hyperglycemic drugs have been 
introduced shordy before or after year 2000. Since then, both 
older and newer drugs were available for anti-hyperglycemic 
treatment in Germany. DPP4-Inhibitors were introduced in 2007 
and were not yet used by patients from both cohorts (time of 
examination 2005/2006-2008). Furthermore, pioglitazone was 
under restriction only after 2011, so that this drug was stiU 
reimbursed by statutory health insurances at the time of both 
surveys. 

Socioeconomic measures 

In our main analysis, we used educational level as indicator of 
socioeconomic position, as many analyses [19-21]. Educational 
level was assessed by highest self-reported schooling degree 
achieved at baseline examination. A dichotomous variable was 
created to indicate high and low educational level. Low 
educational level was assumed if only junior high school was 
attended or if no schooling degree has been achieved. High 
educational level was defined by completed high (higher educa- 
tional entrance qualification, advanced technical college entrance) 
or middle educational graduation (general certificate of secondary 
education or polytechnic grammar school, POS). In other words, 
completed 1 0 years of schooling or more were classified as high 
educational level. 

In the former German Democratic Republic (GDR), a POS 
degree before 1965 was obtained after 8 years of schooling. In 
1965 the schooling system was changed and 10 years were needed 
to achieve a POS degree. Three participants with a POS degree 
(in HNR) born before 1951 were excluded from the analysis, 
because of the limited comparability. 

Information on monthly net household income as well as on 
household size was obtained from personal interviews. Following 
the example of earlier studies within the DIAB-CORE consor- 
tium, we calculated the equivalent income according to the 
Luxembourg Income Study (income/household size) [22,23]. 

Anthropometry, blood pressure, HbAlc, history of strol<e, 
myocardial infarction, and cardiovascular medication 

Body mass index was calculated from measured height and 
weight. In both studies, systolic and diastolic blood pressure was 
measured by trained personal using a validated automatic device 
(OMRON HEM 705-CP, OMRON Corporation, Hoofdorp, The 
Netherlands). Three independent blood pressure measurements 
were taken with a 3-minute pause in a sitting position on the right 
arm. The mean of second and third measurement was used for the 
current analyses. 

In HNR, Hb A 1 c was measured by latex agglutination inhibition 
in EDTA whole blood using the ADVIA Chemistry System. In 
KORA F4, EDTA plasma was analyzed by high performance 
liquid chromatography using Menarini HA-8160. Due to these 
different assessment methods HbAlc values were not considered 
as statistically comparable between both studies. Nonetheless, we 



included HbAlc measurements in the regression models especially 
to determine its confounding effect in stratified analyses. 

History of stroke and myocardial infarction was assessed by 
participant's self-reports ("Did you ever have a myocardial 
infarction/stroke diagnosed by a physician?"). Cardiovascular 
treatment was taken from self-reported medication as described 
above. ATC code "C" indicated any cardiovascular treatment. 

Insurance status, family status and lifestyle measures 

Insurance status was assessed in both studies according to 
patients' self-reports. A binary variable was created separating 
persons who were privately health insured from those who were 
statutory health insured. The family situation and marital status 
were assessed and a dichotomous variable was created for hving 
with a partner (yes/no). 

Smoking habits were assessed in an interview situation in both 
studies. A dichotomous variable was created separating current 
from ex- and never-smokers. Current smokers needed to smoke at 
least one cigarette per day. Ex-smokers had smoked at least one 
cigarette per day in the past, but had quit smoking at least one year 
ago. Never-smokers had never smoked or had smoked only 
occasionally (< 1 cigarette / day). 

In KORA F4, the physical activity level was estimated l)ased on 
self-reported time per week spent on sports activities during leisure 
time in summer and winter. Participants were considered 
physically active if they participated in sports for at least one 
hour per week and as inactive if they were active for < 1 hour per 
week in summer or winter. In HNR, individuals were also 
considered as active, if they were active for at least one hour per 
week but without differentiation between summer and winter 
activity. 

Thresholds for high alcohol consumption were defined for men 
and women (>20 g/day in women and >40 g/day in men). The 
calculation of daily alcohol amount was based on weekly 
consumption of beer, wine and liquor according to Kraus and 
Augustin [24]. 

Statistical analysis 

For descriptive analyses, means and standard deviations of all 
continuous variables as well as proportions and numbers of all 
categorical covariates were computed in the total population as 
well as stratified by study and educational level. Likewise, 
proportions of treatment with anti-hyperglycemic pharmaceutical 
components were calculated. 

We performed two evaluations of the association between 
educational level and regional disparities with anti-hyperglycemic 
medication: First, the association with any medication was 
examined and prevalence ratios (PR) were estimated following 
Zhou et al. by multivariable poisson regression models with a 
robust error variance using log link function [25]. Second, this 
association was examined with newer medication as outcome 
among participants with any anti-hyperglycemic medication. This 
methodological approach was chosen due to a high prevalence of 
both outcomes, whereby an odds ratio calculated from logistic 
regression models would considerably overestimate the true effect 
[26]. Univariable models for study, educational level (as main 
predictors) and all potential confounders were fitted respectively. 
Additionally, three models were fitted adjusting for (1) age at 
examination (one year difference in the age groups compared), sex 
(male vs. female), diabetes duration (years); (2) variables of model 1 
plus BMI (kg/ m^), diastolic and systolic blood pressure (mmHG), 
HbAlC (% and mmol/mol), stroke (yes vs. no) and myocardial 
infarction (yes vs. no) in the past; (3) variables of model 2 plus 
lifestyle factors, i.e. hving with a partner (yes vs. no), sports 



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Regional Differences in Anti-Hyperglycemic Treatment 



activities (yes vs. no), current smoking [yen \s. no), liigli alcohol 
consumption (yes vs. no) and private health insurance (vs. statutory 
health insurance). In sensitivity analyses, all analyses were repeated 
with income as measure for socioeconomic position. All models 
were performed in the total population with type 2 diabetes as well 
as stratified by region. 

Analyses were performed using SAS statistical software version 
9.3 (SAS Institute Inc., Gary, NC, USA). 

Results 

Description of the study population 

Table 1 shows the characteristics and patterns of anti- 
hyperglycemic treatment of participants with type 2 diabetes 
stratified by region and education. In comparison to KORA F4, 
participants from HNR more frequently had a higher education, 
were slightly younger at examination and at diagnosis of diabetes, 
had a shorter duration of diabetes, had higher systolic and diastolic 
blood pressure measurements along with a slightly lower BMI. 
Male preponderance was particularly seen among participants 
with high education in both studies. Living with a partner and 
smoking were more common in HNR while high alcohol 
consumption, sports activities and being privately health insured 
were more frequent in KORA F4 especially in participants with 
high education. Regarding diabetes related complications, stroke 
and cardiovascular treatment were more common in HNR while 
percentages for myocardial infarction were similar in both studies. 
Individuals with low education more often had these complications 
in both studies. For HbAlc (KORA F4: plasma; HNR: whole 
blood), similar values were found in all strata. 

Treatment groups 

About three fourths of all individuals (N = 499) were treated 
with any anti-hyperglycemic treatment among these 47.7% with 
metformin, 25.4% with sulfonylureates, and 2().l"'o with any 
insulin. In KORA F4, more participants received anti-hypergly- 
cemic treatment than in HNR, in particular individuals with low 
education. This pattern was similar for oral anti-hyperglycemic 
treatment and for treatment with any insulin. 

Almost one fourth of those receiving any anti-hyperglycemic 
medication were treated with newer anti-hyperglycemic drugs. 
This proportion was higher in KORA F4 than in HNR. In 
contrast to any anti-hyperglycemic treatment, the frequency of 
newer anti-hyperglycemic treatment was substantially higher 
among participants with high education. These findings were 
consistent when considering solely newer insulin analogues as well 
as newer oral drugs. 

Prevalence of insulin as monotherapy did not differ between 
both studies, while this proportion was substantially higher among 
individuals with low education in KORA F4 (13.8% vs. 2.0%) 
(data not shown). 

Determinants of any anti-hyperglycemic treatment in the 
total population 

The results of regression models modeling factors associated 
with any anti-hyperglycemic medication are shown in table 2. In 

univariable analysis, study location was associated with anti- 
hyperglycemic medication in such a way that KORA F4 
participants had a moderate but significandy higher probability 
to receive anti-hyperglycemic drugs than participants in HNR 
(PR:1.14, 95% CI: 1.05-1.24). Adjustment for education, age at 
examination, sex, diabetes duration, BMI, diastolic and systolic 
blood pressure, HbAlC, clinical variables (previous myocardial 
infarction or stroke), lifestyle factors (living with partner, sports 



activities, smoking, alcohol consumption) and insurance status 
(PR: 1.12; 1.02-1.22) did not alter the results substantially. 

Participants with low educational level tended to have a higher 
albeit not statistically significant probability to receive anti- 
hyperglycemic medication in univariable analysis (PR: 1.10; 
0.99-1.22) as weU as in the fully adjusted model (PR 1.10; 0.98- 
1.23). 

In multivariable models, participants with longer diabetes 
duration had a higher probability to be treated with anti- 
hyperglycemic drugs (PR and corresponding 95% CI for each 
year increase in diabetes duration: 1.01; 1.01-1.02). Likewise, the 
elevation of one unit of HbAlC -value increased this probability 
(PR(%): 1.12; 1.08-1.16; (mmol/mol): 1.010; 1.007-1.014). 
Demographic variables such as age and sex as well as all other 
clinical variables (i.e. blood pressure, previous stroke and 
myocardial infarction), BMI, lifestyle factors (living with partner, 
sports activities, current smoker, high alcohol consumption) as well 
as insurance status had no impact on receiving anti-hyperglycemic 
treatment. 

When stratifying analysis for study region, lower educational 
level was positively associated with any anti-hyperglycemic 
medication in both studies in the fuUy adjusted model, however 
not reaching level of significance neither in KORA F4 (PR: 1.1 7; 
0.98-1.39) nor in HNR (PR: 1.08; 0.93-1.26). In contrast, tiie 
associations with diabetes duration and HbAlC remained 
significant in both study regions. Interaction between education 
and study region was not significant (p-value for multivariable 
adjusted interaction term: p = 0.68). 

Determinants of newer anti-hyperglycemic treatment 
among those with any anti-hyperglycemic treatment 

Table 3 shows the results of the regression models modeling 
factors associated with newer anti-hyperglycemic medication 
among the 499 participants with any anti-hyperglycemic medica- 
tion. 

In univariable models, KORA F4 participants had a signifi- 
candy higher probability to receive newer glucose lowering drugs 
(PR 1.43; 95% CI: 1.05-1.96). This association remained 
significant after adjustment for all potential confounders (PR: 
1.52; 1.10-2.11). In contrast, persons with low educational level 
were significantly less frequentiy treated with newer anti-h^per- 
glycemic drugs compared to those with high education (univari- 
able PR: 0.66; 0.48-0.91), which was also true after multivariable 
adjustment: (PR: 0.68; 0.47-0.996). 

In fully adjusted models diabetes duration (PR: 1.03; 1.02-1.05), 
HbAlC (PR: 1.02; 95% CI: 1.004-1.03) and being private health 
insured (PR: 2.05; 95% CI: 1.25-3.36) was also positively 
associated with newer anti-hyperglycemic medication. Again, age 
and sex were not associated with newer anti-hyperglycemic 
treatment as well as BMI, blood pressure, previous myocardial 
infarction and lifestyle factors. 

After stratification for study region, the probability to receive a 
newer anti-diabetic treatment among participants with lower 
education was significandy lower only in HNR (KORA F4: 0.82; 
95% CI: 0.50-1.37; HNR: 0.57; 0.33-0.98). Regarding covariates, 
an inconsistent pattern was found. In HNR, HbAlC (PR (%): 
1.27; 1.05-1.54; (mmol/mol): 1.023; 1.005-1.041) and being 
privately health insured were positively associated with newer anti- 
hyperglycemic treatment (PR: 2.44; 1.23^.81), while in KORA 
F4, diabetes duration increased this probability (PR: 1.05; 1.03- 
1.08). In contrast, negative associations were obser\'ed in KORA 
F4 with diastolic blood pressure (PR for one unit increase: 0.94; 
0.91-0.98) as well as previous myocardial infarction (PR: 0.25; 



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Regional Differences in Anti-Hyperglycemic Treatment 

















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June 2014 | Volume 9 | Issue 6 | e99773 



Regional Differences in Anti-Hyperglycemic Treatment 



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0.07-0.87). The interaction term between education and study 
region was not significant (p = 0.42). 

As furtlier clinical variables, HbAl and diabetes duration 
increased the possibility to receive (any or newer) anti-hypergly- 
cemic medication in both studies. Adding both variables to the 
models did not confound the association of anti-hyperglycemic 
treatment with region or SES. 

Sensitivity analysis 

When we used equivalent income (income/household members) 
instead of education, the probability of receiving any anti- 
hyperglycemic medication was not associated with SES in the 
whole population as well as in stratified analyses (PR for an 
increase of 100€ income: whole population 1.00; 0.99-1.01; 
KORA F4 0.99; 0.98-1.005; HNR 1.00; 0.99-1.01). Income was 
also associated with newer anti-hyperglycemic treatment in 
KORA F4 (PR for an increase of 100€ income: whole population 
1.02; 0.996-1.04; KORA F4 1.06; 1.03-1.09; HNR 1.00; 0.98- 
1.03). In the fully adjusted models PRs of all other variables did 
not materially change (data not shown). 

Discussion 

IVlain findings and implications 

In this cross-sectional examination based on pooled individual 
data from two population-based studies - one in the south (KORA 
F4) and one in the west (HNR) of Germany - the probability to 
receive anti-hyperglycemic drugs as well as to receive newer 
glucose lowering drugs such as insulin analoga, TZDs, or glinides 
was substantially higher in the south of Germany. Regarding 
socioeconomic differences, individuals with lower educational level 
tended to have a higher probability to receive anti-hyperglycemic 
drugs than their better educated counterparts. However, the 
association was not significant. Among those with any anti- 
hyperglycemic medication, individuals with lower educational 
level had a significantly lower probability to receive newer anti- 
hyperglycemic drugs than their better educated counterparts. In 
region-stratified analyses the latter effect was only significant in 
HNR, however, the overall pattern was similar in both studies. 
HbAlc and diabetes duration were further independent predictors 
for anti-hyperglycemic medication in both studies. However, this 
association could not explain the regional diflFerences in anti- 
hyperglycemic medication and the difference in high and low SES 
groups (especially HNR). Furthermore, the regional and socio- 
economic differences remained after adjusting for other individual 
factors available for analysis (such as BMI, lifestyle or complica- 
tions such as myocardial infarction and stroke). 

Importantiy, the older anti-hyperglycemic drug metformin 
remains the oral drug of first choice in current clinical guidelines 
[27]. These guideline recommendations emphasize the need for 
individualized treatment decisions which are influenced by clinical 
decisions as well as other patient characteristics and which in 
consequence can be responsible for the prescription of newer anti- 
hyperglycemic drugs. As an example, age, Hbalc levels, expected 
treatment efforts, diabetes related comphcations, and co-morbid- 
ities of patients merit attention. Disadvantages of metformin such 
as gastrointestinal side effects, vitamin B 1 2 deficiency, and chronic 
kidney disease may guide treatment choice towards newer 
medications [27]. A decision for gUtazone includes severe 
adipositas (insulin resistance) [28]. Furthermore, before 2011, 
TZDs (pioglitazone) were not recommended for patients with 
cardiovascular or hepatic disease. However, since 201 1, TZDs are 
under restriction in Germany and are currently not reimbursed by 
statutory health insurances. 



6 



June 2014 | Volume 9 | Issue 6 | e99773 



Regional Differences in Anti-Hyperglycemic Treatment 



Table 2. Factors associated with any anti-hyperglycemic nnedication {N = 662)*. 




Univarlable Model 


Total 


KORA 


HNR 


Prevalence Ratio (95% CI) 


Study (KORA vs. HNR) 


1.14(1.05-1.24) 






Low educational level vs. high 


1.10(0.99-1.22) 


1.16(0.97-1.40) 


1 .07(0.94-1 .22) 


Age at examination (years) 


1.00(1.00-1.01) 


1.01(1.00-1.02) 


1.00(0.99-1.01) 


Male sex vs. female 


1.01(0.93-1.11) 


1.04(0.91-1.19) 


1.01(0.90-1.13) 


Diabetes duration (years) 


1.01(1.01-1.02) 


1.01(1.01 1.02) 


1.01(1.01-1.02) 


BMI (kg/m^) 


1.01(1.00-1.02) 


1.01(1.00-1.02) 


1.01(1.00-1.02) 


Diastolic blood pressure (mmHg) 


1.00(0.99-1.00) 


0.99(0.99-1.00) 


1.00(0.99-1.00) 


Systolic blood pressure (mmHg) 


1.00(1.00-1.00) 


1.00(0.99-1.00) 


1.00(1.00-1.00) 


HbAlc (mmol/mol) 


1.01(1.01 1.01) 


1.01(1.00-1.01) 


1.01(1.01-1.02) 


Previous stroke (yes vs. no) 


0.94(0.78-1.14) 


1.08(0.85-1.38) 


0.91(0.72-1.16) 


Previous Ml (yes vs. no) 


1.01(0.88-1.17) 


1.03(0.85-1.26) 


1.00(0.83-1.20) 


Living with partner (yes vs. no) 


0.95(0.86-1.05) 


0.89(0.79-1.02) 


1.00(0.86-1.15) 


Sports activity <1 h a week (yes vs .no) 


1.05(0.96-1.15) 


1.15(1.01-1.32) 


1.02(0.91-1.14) 


Current smoker (yes vs. no) 


0.91(0.80-1.04) 


1.00(0.82-1.22) 


0.90(0.76-1 .06) 


High alcohol consumption (yes vs. no) 


1.05(0.91-1.22) 


1.00(0.82-1.22) 


1.05(0.85-1.30) 


Private insured (vs. statutory health Insured) 


1.03(0.86-1.22) 


1.07(0.88-1.30) 


0.97(0.73-1.27) 


Basic model 


Low educational level vs. high 


1.09(0.98-1.21) 


1.14(0.95-1.36) 


1.08(0.94-1.23) 


Study (KORA vs. HNR) 


1.13(1.03-1.22) 






Age at examination (years) 


1.00(0.99-1.00) 


1.00(0.99-1.01) 


1.00(0.99-1.00) 


Male sex vs. female 


1.02(0.93-1.11) 


1.05(0.93-1.19) 


1.00(0.89-1.12) 


Diabetes duration (years) 


1.01(1.01-1.02) 


1.01(1.01-1.02) 


1.02(1.01-1.02) 


"Clinical model": Basic model + BMI + comorbidities + Blood Pressure** 


Low educational level vs. high 


1.08(0.97-1.21) 


1.17(0.98-1.39) 


1.06(0.93-1.22) 


Study (KORA vs. HNR) 


1.13(1.03 1.23) 






Age at examination (years) 


1.00(0.99-1.01) 


1.00(0.99-1.01) 


1.00(0.99-1.01) 


Male sex vs. female 


1.04(0.95-1.14) 


1.11(0.96-1.29) 


1.01(0.90-1.13) 


Diabetes duration (years) 


1.01(1.01-1.02) 


1.01(1.00-1.02) 


1.01(1.01-1.02) 


BMI (kg/m^) 


1.01(1.00-1.01) 


1.01(1.00-1.02) 


1.00(0.99-1.01) 


Diastolic blood pressure (mmHg) 


1.00(0.99-1.00) 


1.00(0.99-1.00) 


1 .00(0.99-1 .00) 


Systolic blood pressure (mmHg) 


1.00(1.00-1.00) 


1.00(0.99-1.00) 


1.00(1.00-1.01) 


HbAlc (mmol/mol) 


1.01(1.01-1.01) 


1.01(1.00-1.01) 


1.01(1.01-1.01) 


Previous stroke (yes vs. no) 


0.92(0.76-1.12) 


1.02(0.81-1.30) 


0.90(0.71-1.15) 


Previous Ml (yes vs. no) 


0.98(0.85-1.12) 


0.92(0.75-1.13) 


1.01(0.84-1.22) 


Full model: "Clinical model"+ Lifestyle + Insurance status*** 


Low educational level vs. high 


1.10(0.98-1.23) 


1.17(0.98-1.39) 


1.08(0.93-1.26) 


Study (KORA vs. HNR) 


1.12(1.02 1.22) 






Age at examination (years) 


1.00(0.99-1.01) 


1.00(0.99-1.01) 


1.00(0.99-1.01) 


Male sex vs. female 


1.05(0.95-1.17) 


1.12(0.95-1.31) 


1.02(0.90-1.17) 


Diabetes duration (years) 


1.01(1.01-1.02) 


1.01(1.00-1.02) 


1.01(1.00-1.02) 


BMI (kg/m^) 


1.00(1.00-1.01) 


1.01(1.00-1.02) 


1.00(0.99-1.01) 


Diastolic blood pressure (mmHg) 


1.00(0.99-1.00) 


1.00(0.99-1.01) 


1.00(0.99-1.01) 


Systolic blood pressure (mmHg) 


1.00(1.00-1.00) 


1.00(0.99-1.00) 


1.00(1.00-1.01) 


HbAlc (mmol/mol) 


1.01(1.01-1.01) 


1.01(1.00-1.01) 


1.01(1.01-1.02) 


Previous stroke (yes vs. no) 


0.90(0.73-1.11) 


1.02(0.80-1.30) 


0.87(0.67-1.13) 


Previous Ml (yes vs. no) 


1.01(0.89-1.16) 


0.93(0.75-1.15) 


1 .08(0.90-1 .29) 


Living with partner (yes/no) 


0.98(0.88-1 .09) 


0.92(0.80-1.06) 


1.02(0.87-1.20) 


Sports activity <1 h a week (yes vs. no) 


1.02(0.93-1.11) 


1.09(0.97-1.23) 


0.98(0.87-1.10) 



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7 



June 2014 | Volume 9 | Issue 6 | e99773 



Regional Differences in Anti-Hyperglycemic Treatment 



Table 2. Cont. 





Univarlable Model 


Total KORA 




HNR 


Prevalence Ratio (95% CI) 


Current snnol<er (yes vs. no) 


0.90(0.79-1.04) 1.00(0.81- 


-1.24) 


0.86(0.72-1 .02) 


High alcohol consumption(yes vs. no) 


1.05(0.90-1.24) 1.03(0.85- 


-1.24) 


1.14(0.88-1.47) 


Private insured (vs. statutory health insured) 


1.07(0.89-1.27) 1.06(0.85- 


-1.32) 


1.04(0.78-1.38) 



*Results are prevalence ratios (95%CI) calculated from poisson regression models with robust error variance as proposed by Zhou et al.[25] 

Abbreviations: KORA= Cooperative Health Research in the Region of Augsburg study; HNR= Heinz Nixdorf Recall study; Ml= myocardial infarction. 

** 10 missing values (in HbAlc} 

*** 37 missing values 

doi:l 0.1 371 /journa!.pone.0099773.t002 



We controlled for some of these variables in our study. 
However, detailed information on complications other than 
myocardial infarction and stroke were not available in a highly 
comparable way for a pooled analysis, so that individual treatment 
decisions were irreproducible. Nevertheless, the south of Germany 
is a region with lower overall mortality, lower blood pressure and 
lower type 2 diabetes prevalence than the west [29,30]. Thus, 
given the overall trend for a healthier population in the south, 
mere clinical decisions are not likely to have caused the regional 
differences we found. 

We could not find any explanations for our fmdings. In 
Germany, as well as in most western European countries, almost 
all individuals are members of a health insurance and have almost 
free access to the majority of medical services. Exceptions are 
medications given as over-the-counter medication and for diseases 
with low severity, e.g. cold, which are paid by the patients. 
Overall, private expenditures account for about 1 5 % of the health 
care expenditures [7]. Diabetes treatment in Germany should be 
rather standardized, in particular since the introduction of disease 
management programs. However, regional differences with 
respect to health care services are likely. For example in more 
rural regions, the availability of specialized diabetes care might be 
lower compared to urban areas [31]. Respective analyses are 
planned for the future. In addition, local health care practice 
including e.g. the screening frequency or generally the awareness 
of the disease in a population might affect the proportion of 
undiagnosed diabetes in a region possibly also causing regional 
differences in sample characteristics. 

It could be argued that regional and social discrepancies for 
newer glucose lowering drug use could be mediated at least in part 
by health insurance status. Persons with a high income or those 
who are self-employed are free to take out private health insurance 
covering extra services of medical care. While statutory health 
insurances impose a limit on GPs and specialized diabetes 
practitioners for prescriptions, private health insurance companies 
are more likely to accept the higher costs for newer medications. 
Besides cost reasons, individuals with private health insurance 
might differ from those insured statutorily in such a way that they 
might participate more actively in treatment decisions and claim 
for newer medications. 

In our study, we found a higher proportion of anti-hypergly- 
cemic drug intake among privately health insured persons 
compared to statutorily health insured participants, which was 
significantiy increased for newer anti-hyperglycemic medication. 
Adjusting for health insurance status did not alter the association 
between education and anti-hyperglycemic medication substan- 
tially. These findings could emphasize that persons with a higher 



education in general might receive newer drugs more frequendy 
irrespective of their status of insurance. 

Interestingly, when we used equivalent income as an indicator 
for socioeconomic status, the probability for receiving any anti- 
hyperglycemic medication was not associated with SES. However, 
there was an association with newer anti-hyperglycemic medica- 
tion which is also more expensive. Similar results were reported in 
a recent study from Sweden, where drug utilization was associated 
with education, but not with income [32] . The authors could not 
explain their findings. They suggest that medication may be 
influenced particularly by the interaction between physician and 
patient, and that this interaction may depend on patients' 
education more than on patients' income level. 

Comparison to other studies 

Despite the interest in geographic differences in health care 
spending and treatment patterns, literature on the contribution of 
structural deprivation and individual socioeconomic status on anti- 
hyperglycemic treatment is scarce. Social gradients in treatment 
with certain medications or diet alone have been found earlier in a 
Canadian study [33]. Prescription of metformin and sulfonylureas 
was higher in lower income groups, while "diet-alone" was more 
often treatment option in higher income quintiles than in lower 
ones. Another Canadian study based on reimbursement data 
indicated, that high income groups were more likely to receive 
restricted medications such as thiazolidinediones (TZDs) com- 
pared to low income groups [6], similar to our study. The authors 
could not explain their finding. Regional disparities in prescription 
patterns based on insurance data have been described earlier for 
the prescription prevalence of antibiotic use. A recent German 
study showed a regional variation of 19-53% of antibiotic use in 
children which was partly explained by regional deprivation 
(especially by regional income and occupational deprivation) [34] . 

Structural differences of health care supply which have recendy 
been reported for Germany might also be relevant for our findings 
[35]. The authors analyzed if regional health care utilization met 
the expected needs (equity index = 1). They could show that 
factors of health care supply such as physician density and 
physician contacts explained 49% of health care utilization. A high 
physician density and a high number of physician contacts was 
associated with a higher health care utilization beyond the 
expected needs (equity index below 1). On the other hand, a high 
number of social welfare recipients in a region was associated with 
a lower utilization. Regarding our study areas, for Augsburg and 
its rural surroundings a low equity index was calculated (utilization 
exceeded the needs) while the equity index was close to 1 in the 
urban areas of the HNR study. Therefore, structural differences by 
region such as a higher physician density and a higher number of 



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June 2014 | Volume 9 | Issue 6 | e99773 



Regional Differences in Anti-Hyperglycemic Treatment 



Table 3. Factors associated with newer anti-hyperglycem 
medication (N=499) (newer vs. older medication)*. 


c medication a 


mong participants with 


any anti-hyperglycemic 




Univarlable Model 


Total 


KORA 


HNR 


Prevalence Ratio (95% CI) 


Study (KORA vs. HNR) 


1.43(1.05-1.96) 






Low educational level vs. high 


0.66(0.48-0.91) 


0.78(0.47-1.31) 


0.57(0.38-0.86) 


Age at examination (years) 


1.00(0.98-1.02) 


1.00(0.97-1.03) 


0.99(0.96-1.02) 


Male sex vs. female 


0.95(0.69-1.30) 


0.72(0.45-1.15) 


1.17(0.76-1.81) 


Diabetes duration (years) 


1.04(1.03-1.06) 


1.06(1.04-1.08) 


1.03(1.01 1.05) 


BMI (kg/m^) 


1.02(0.99-1.04) 


1.03(0.99-1.07) 


1.01(0.97-1.04) 


Diastolic blood pressure (mmHg) 


0.98(0.97-1.00) 


0.96(0.94 0.99) 


1.00(0.98-1.02) 


Systolic blood pressure (mmHg) 


1.00(0.99-1.00) 


0.99(0.98-1.01) 


1.00(0.99-1.01) 


HbAlc (mmol/mol) 


1.02(1.01-1.03) 


1.01(0.99-1.03) 


1.02(1.01 1.03) 


Previous stroke (yes vs. no) 


0.38(0.13-1.12) 


n.e. 


0.58(0.20-1.70) 


Previous Ml (yes vs. no) 


0.81(0.45-1.44) 


0.37(0.10-1.37) 


1.14(0.60-2.17) 


Living with partner (yes vs. no) 


1.07(0.72-1.58) 


1.35(0.74-2.47) 


0.95(0.56-1.59) 


Sports activity <1 h a week(yes vs. no) 


1.02(0.74-1.41) 


0.87(0.54-1.39) 


1.20(0.77-1.87) 


Current smoker (yes vs. no) 


0.77(0.46-1.26) 


0.32(0.09-1.22) 


1.05(0.60-1.82) 


High alcohol consumption (yes vs. no) 


1.14(0.67-1.94) 


0.86(0.39-1.91) 


1.33(0.65-2.72) 


Private insured (vs. statutory health insured) 


2.00(1.28-3.12) 


1 .49(0.77-2.87) 


2.40(1.31-4.39) 


Basic model 


Low educational level vs. high 


0.60(0.44-0.83) 


0.62(0.37-1.02) 


0.56(0.37-0.87) 


Study (KORA vs. HNR) 


1.50(1.10-2.03) 






Age at examination (years) 


0.99(0.97-1.01) 


0.98(0.95-1.01) 


0.99(0.96-1 .02) 


Male sex vs. female 


0.87(0.65-1.18) 


0.66(0.43-1.02) 


1.07(0.70-1.63) 


Diabetes duration (years) 


1.05(1.03-1.06) 


1.07(1.04-1.09) 


1.04(1.02-1.06) 


"Clinical model": Basic model + BMI + comorbidities + Blood Pressure** 


Low educational level vs. high 


0.61 (0.44-0.85) 


0.77(0.47-1.24) 


0.52(0.33-0.80) 


Study (KORA vs. HNR) 


1.48(1.08-2.02) 






Age at examination (years) 


0.99(0.97-1.02) 


0.96(0.93-1.00) 


1.01(0.98-1.05) 


Male sex vs. female 


0.94(0.67-1.31) 


0.94(0.57-1.54) 


1.05(0.67-1.65) 


Diabetes duration (years) 


1.04(1.02-1.06) 


1.06(1.03-1.08) 


1.03(1.01-1.05) 


BMI (kg/m^) 


1.01(0.98-1.04) 


1.04(1.00-1.08) 


1.00(0.96-1.03) 


Diastolic blood pressure (mmHg) 


0.99(0.96-1.01) 


0.95(0.91-0.98) 


1.01(0.98-1.04) 


Systolic blood pressure (mmHg) 


1.00(0.99-1.01) 


1.01(0.99-1.03) 


1.00(0.98-1.01) 


HbAlc (mmol/mol) 


1.02(1.01-1.03) 


1.01(0.99-1.03) 


1.03(1.01-1.04) 


Previous stroke (yes vs. no) 


0.30(0.08-1.15) 


n.e. 


0.47(0.13-1.70) 


Previous Ml (yes vs. no) 


0.78(0.43-1 .42) 


0.26(0.07-0.98) 


1.30(0.66-2.57) 


Full model: "Clinical moder'+ Lifestyle + Insurance status*** 


Low educational level vs. high 


0.68(0.47-1.00) 


0.82(0.50-1.37) 


0.57(0.33-0.98) 


Study (KORA vs. HNR) 


1.52(1.10-2.11) 






Age at examination (years) 


0.99(0.97-1 .02) 


0.96(0.93-1.00) 


1.01(0.98-1.05) 


Male sex vs. female 


0.86(0.59-1 .25) 


0.78(0.45-1.35) 


1.01(0.59-1.72) 


Diabetes duration (years) 


1.03(1.02-1.05) 


1.05(1.03-1.08) 


1.02(1.00-1.05) 


BMI (kg/m^) 


1.02(0.99-1.05) 


1.04(0.99-1.08) 


1.01(0.97-1.05) 


Diastolic blood pressure (mmHg) 


0.99(0.96-1.01) 


0.94(0.91-0.98) 


1.01(0.97-1.04) 


Systolic blood pressure (mmHg) 


1.00(0.99-1.02) 


1.01(0.99-1.03) 


1.00(0.98-1.02) 


HbAlc (mmol/mol) 


1.02(1.00-1.03) 


1.02(1.00-1.04) 


1.02(1.00-1.04) 


Previous stroke (yes vs. no) 


0.17(0.02-1.17) 


n.e. 


0.30(0.04-2.03) 


Previous Ml (yes vs. no) 


0.82(0.45-1.50) 


0.25(0.07-0.87) 


1.42(0.71-2.85) 


Living with partner (yes vs. no) 


1 .29(0.83-2.00) 


1.57(0.84-2.94) 


0.97(0.52-1.83) 


Sports activity <1 h a week(yes vs. no) 


1.03(0.74-1.43) 


0.88(0.55-1.39) 


1.12(0.69-1.81) 



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Regional Differences in Anti-Hyperglycemic Treatment 



Table 3. Cont. 





Univarlable Model 


Total KORA 


HNR 


Prevalence Ratio (95% CI) 


Current snnol<er (yes vs. no) 


0.82(0.48-1.40) 0.38(0.11-1.34) 


1.11(0.59-2.09) 


High alcohol consumption(yes vs. no) 


0.84(0.42-1.67) 0.81(0.35-1.83) 


0.82(0.29-2.29) 


Privately insured (vs. statutory health insured) 


2.05(1.25-3.36) 1.67(0.80-3.49) 


2.44(1.23-4.81) 



*Results are prevalence ratios (95%CI) calculated from poisson regression models with robust error variance as proposed by Zhou et al. [25]. Significant differences (p> 
0.05) are highlighted in bold. 

Abbreviations: KORA= Cooperative Health Research In the Region of Augsburg study; HNR= Heinz NIxdorf Recall study; Ml= myocardial infarction, n.e. = not 
estimable. 



** 11 missing values (in HbAlc). 

*** 45 missing values. 

doi:1 0.1 371 /journal.pone.0099773.t003 



physician contacts might be important factors contributing to the 
higher overall anti-hyperglycemic medication use in KORA F4 
which should be addressed in further studies. 

Strenghts and limitations 

Our study has several limitations. First, we could not examine if 
direct contracts between general practitioners and health insur- 
ance companies, which vary across regions, might have had an 
impact on treatment decisions. Second, as described above, 
clinical information on participants was limited. Thus, we could 
not evaluate if treatment patterns follow clinical guidelines and 
correspond with indications for newer treatment options. Further- 
more, cases with cardiovascular events (myocardial infarction, 
stroke) were too low in some subgroups so that statistical power 
was insufficient to detect associations with treatment decisions. 
Finally, some variables, such as HbAlc, were not exactiy 
comparable between the two studies. 

The strengths of our study are highly standardized measure- 
ment techniques carried out by trained personnel (e.g. for 
anthropometry and blood pressure) and the application of very 
similar, standardized interviews and questionnaires. Sampling 
frames of both population-based studies aimed for a high 
representativeness of the data. Furthermore, both studies used a 
similar scanning system to assign unique pharmaceutical identifiers 
(ATC codes) to the medication packages brought to the interview 
date. 



In conclusion, we found regional disparities in any and in newer 
anti-hyperglycemic treatment in Germany. Lower social status was 
also associated with a lower probability to receive newer anti- 
hyperglycemic drugs which was especially observed in the Ruhr 
area (HNR). Overall, these differences were not explained by age, 
sex, BMI, and lifestyle factors such as sports activities or smoking 
as well as insurance status. Of note, the disparities in treatment 
with newer anti-hyperglycemic drugs we found do not imphcate 
regional or social disparities in quality of care. Further research is 
needed to explain these findings. Especially, studies are warranted 
that include a larger number of patients and further geographic 
regions. 

Acknowledgments 

We thank aU study participants without whom this work would not have 
been possible. We acknowledge the support of the Sarstedt AG & Co. 
concerning laboratory equipment. We thank the investigative group and 
the study staff of the kleinz Nixdorf Recall Study. 

Author Contributions 

Conceived and designed the experiments: AI. Analyzed the data: HC TT. 
Contributed reagents/materials/analysis tools: CM AM RH BT SM AAM 
NP BK US RE KHJ. Wrote the paper: TT HC. Pooling of data: HG TT 
WR AX IMR WM MS. 



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